English word memory training system based on artificial intelligence and voice recognition technology

Abstract

The invention provides an English word memory training system based on artificial intelligence and speech recognition technology, comprising: a word learning and memorizing module which is connected with the Internet, obtains and provides a complete word library including common English words, four-level words, six-level words, examination and research words and the like, and is used for learning and memorizing the English words, the four-level words, the six-level words, the examination and research words and the like; the user selects a favorite training mode to learn words for memory training; and the pronunciation evaluation module monitors the pronunciation of the user by adopting a voice recognition technology, evaluates the pronunciation accuracy, analyzes the pronunciation of the user, feeds back in time, and helps the user to correct pronunciation errors in real time. A user can select a favorite training mode through the word learning and memorizing module, real-time correction processing is carried out according to practice pronunciation of the user, personalized learning suggestions and recommendations are provided according to characteristics and requirements of the user, and the adaptability of learning and memorizing training is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of English learning, and in particular to an English word memory training system based on artificial intelligence and speech recognition technology. Background Art

[0002] English is a universal language, widely used in international communication, business, science and technology, cultural exchange and other fields. With the inclusion of English in the scope of education and the continuous strengthening of the trend of globalization, people are also paying more and more attention to the study of English. In the current era of scientific and technological development, artificial intelligence and speech recognition technology are constantly improving. People are beginning to use artificial intelligence and speech recognition technology to assist in the learning and memory training of English words. Conventional English word memory training systems are generally mobile applications. Users operate by using mobile devices, and learn and memorize by users' customized learning goals and plans. However, the existing English word memory training system is difficult to assist users in correcting word pronunciation in real time. At the same time, in the learning process, it can only rely on the learning goals and plans set by the users themselves to advance. When the user's ability is difficult to keep up with his own learning plan, it is impossible to make adaptive adjustments to different user groups.

[0003] Therefore, in order to solve the above problems, an English word memory training system based on artificial intelligence and speech recognition technology is developed which has the ability of real-time pronunciation correction and can perform adaptive learning adjustment for different user groups. Summary of the invention

[0004] In order to overcome the shortcomings of existing English word memory training systems that are difficult to assist users in correcting word pronunciation in real time, and that during the learning process, the systems can only rely on the learning goals and plans set by the users themselves to advance, and that when the users' abilities are unable to keep up with their own learning plans, adaptive adjustments cannot be made for different user groups, the present invention provides an English word memory training system based on artificial intelligence and speech recognition technology that has real-time pronunciation correction capabilities and can perform adaptive learning adjustments for different user groups.

[0005] The technical solution of the present invention is: an English word memory training system based on artificial intelligence and speech recognition technology, comprising: A word learning and memory module, which is connected to the Internet to obtain and provide a complete word library, including common English words, CET-4 words, CET-6 words, and postgraduate entrance examination words, etc. Users can learn words and perform memory training by selecting a favorite training mode; A pronunciation evaluation module, which uses speech recognition technology to monitor the user's pronunciation, and performs pronunciation accuracy evaluation processing, analyzes the user's pronunciation and provides timely feedback to help the user correct pronunciation errors in real time; The progress tracking module is configured to record the user's learning progress and performance, and provide personalized learning suggestions and recommendations based on the user's characteristics and needs, and make adaptive adjustments to the user's level and learning goals.

[0006] As a preferred embodiment of the present invention, the word learning and memory module includes: A database, the database is connected to the Internet, and acquires and establishes a complete English word model; A user interface, wherein the user interface adopts a simple operation mode so that the user will not have difficulty in using the user interface, and the user can select the training mode of his / her preference in the user interface; A word range selection unit, wherein the word range selection unit is configured to select a word range suitable for the user's current learning range before performing system learning and memory, such as common English words, CET-4 words, CET-6 words, and postgraduate entrance examination words.

[0007] As a preferred embodiment of the present invention, the user interface includes a training mode selection unit, and the training mode selection unit includes: A word-by-word reading mode, in which the user follows the system to read the word word by word, so that the user can grasp the pronunciation of the word more accurately; Dictation mode, which includes Chinese listening to English, English listening to Chinese, word splitting, etc., so that users can deeply memorize words during the learning and memorization process and deepen the memory effect; Fill-in-the-blank mode, which is configured to display part of the word content or Chinese translation, and the user completes the spelling of the word according to the prompt; A difficulty selection unit is configured so that the user can adjust the difficulty of the above-mentioned mode according to his / her own learning situation and acceptance ability, so that the training difficulty is more matched with his / her own ability.

[0008] As a preferred embodiment of the present invention, the pronunciation evaluation module includes: A speech recognition engine, wherein the speech recognition engine uses speech recognition technology to capture and analyze the user's pronunciation; A natural language processing module serves the speech recognition engine and is configured to enable the speech recognition engine to understand, process and generate human language.

[0009] As a preferred embodiment of the present invention, the natural language processing module includes: A word embedding model, which is used to represent text as a low-dimensional vector to capture the semantic information of words; Sequence models, which are used for tasks such as text classification, sentiment analysis, and named entity recognition; The language model uses training and fine-tuning strategies to further optimize and improve language understanding.

[0010] As a preferred embodiment of the present invention, the progress tracking module includes a personalized adjustment unit, which is configured to record the user's learning progress and performance and establish a corresponding data model, and then perform analysis and processing based on the data model to provide corresponding adjustment feedback for different user groups.

[0011] As a preferred embodiment of the present invention, the personalized adjustment unit performs the personalized recommendation process in the following main steps: Step 1: Data collection and preprocessing: collect user-related data, such as user historical behavior data and personal data, and then preprocess the user-related data, including data cleaning, feature extraction and conversion; Step 2: User feature representation: construct a user feature vector based on the preprocessed user data to describe the user's feature label; Step 3: Similarity calculation, calculating the similarity between user features and candidate items, such as learning content, learning results, recommended content, etc., using the Euclidean distance algorithm or the cosine similarity algorithm; Step 4: Ranking of candidates: using collaborative filtering algorithm to rank the similarities between candidates and user features for different users; Step 5: Generate personalized suggestions. Based on the ranking results of the candidates, select the top-ranked candidates as personalized suggestions to display to the user, and formulate strategies based on specific learning needs. Step 6: Feedback and iterative update, continuously update user features and similarity calculation models based on user feedback information.

[0012] As a preferred embodiment of the present invention, a content expansion module is also included, and the content expansion module is configured to provide extended information such as sentence examples, synonyms and antonyms, context training, etc. to assist users in understanding and using words.

[0013] The present invention has the following advantages: 1. The present invention allows users to choose their favorite training mode through word learning and memory modules, and performs real-time correction processing on the user's practice pronunciation. At the same time, it provides personalized learning suggestions and recommendations based on the user's characteristics and needs, thereby improving the adaptability of learning and memory training.

[0014] 2. The present invention expands the learning content through the content expansion module and provides various learning expansion information, thereby assisting users to understand and use words and improve learning effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the structure of the present invention.

[0016] Figure 2 It is a structural schematic diagram of the word learning and memory module of the present invention.

[0017] Figure 3 It is a structural schematic diagram of the pronunciation evaluation module of the present invention.

[0018] Figure 4 This is a flow chart of the steps of the personalized recommendation process of the present invention.

[0019] Figure 5 It is a flow chart of the working principle of the present invention. DETAILED DESCRIPTION

[0020] Reference to an embodiment herein means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0021] An English word memory training system based on artificial intelligence and speech recognition technology, such as Figure 1 As shown, it includes a word learning and memory module, a pronunciation evaluation module and a progress tracking module. The word learning and memory module is connected to the Internet to obtain and provide a complete word library, including common English words, CET-4 words, CET-6 words and postgraduate entrance examination words, etc. Users learn words and perform memory training by selecting a favorite training mode. The pronunciation evaluation module uses voice recognition technology to monitor the user's pronunciation, and performs pronunciation accuracy evaluation processing, analyzes the user's pronunciation and provides timely feedback to help users correct pronunciation errors in real time. The progress tracking unit is configured to record the user's learning progress and performance, and provide personalized learning suggestions and recommendations based on the user's characteristics and needs, and adaptively adjusts feedback based on the user's level and learning goals. It also includes a content expansion module, which is configured to provide sentence examples, synonyms and antonyms, context training and other extended information to assist users in understanding and using words.

[0022] like Figure 2As shown, the word learning and memory module includes a database, a user interface and a word range selection unit. The database is connected to the Internet to obtain and establish a complete English word model. The user interface adopts a simple operation method so that the user will not have difficulty in use. The user interface can select the training mode of their preference. The word range selection unit is configured to select a word range suitable for the user's current learning range before conducting system learning and memory, such as common English words, CET-4 words, CET-6 words, and postgraduate entrance examination words, etc. The user interface includes a training mode selection unit, and the training mode selection unit includes a word-by-word reading mode. , dictation mode, fill-in-the-blank mode and difficulty selection unit. The word-by-word reading mode is configured for the user to follow the system to read the words word by word, so that the user can grasp the pronunciation of the words more accurately. The dictation mode is divided into Chinese listening to English, English listening to Chinese, word splitting and other methods, so that the user can deeply memorize the words in the process of learning and memorizing, and deepen the memory effect. The fill-in-the-blank mode is configured to display part of the word content or Chinese translation, and the user completes the spelling of the word according to the prompts. The difficulty selection unit is configured for the user to adjust and select the difficulty of the above modes according to his own learning situation and acceptance ability, so that the training difficulty is more matched with his own ability.

[0023] like Figure 3 As shown, the pronunciation evaluation module includes a speech recognition engine and a natural language processing module. The speech recognition engine uses speech recognition technology to capture and analyze the user's pronunciation. The natural language processing module serves the speech recognition engine and is configured to enable the speech recognition engine to understand, process and generate human language. The natural language processing module includes a word embedding model, a sequence model and a language model. The word embedding model is used to represent text as a low-dimensional vector and capture the semantic information of words. The sequence model is used for tasks such as text classification, sentiment analysis and named entity recognition. The language model adopts training and fine-tuning strategies to further optimize and improve language understanding.

[0024] The progress tracking module includes a personalized adjustment unit, which is configured to record the user's learning progress and performance and establish a corresponding data model. Then, the data model is analyzed and processed to provide corresponding adjustment feedback for different user groups. When the personalized adjustment unit performs personalized recommendation processing, such as Figure 4 As shown, it is divided into the following main steps: Step 1: Data collection and preprocessing: collect user-related data, such as user historical behavior data and personal data, and then preprocess the user-related data, including data cleaning, feature extraction and conversion; Step 2: User feature representation: construct a user feature vector based on the preprocessed user data to describe the user's feature label; Step 3: Similarity calculation, calculating the similarity between user features and candidate items, such as learning content, learning results, recommended content, etc., using the Euclidean distance algorithm or the cosine similarity algorithm; Step 4: Ranking of candidates: using collaborative filtering algorithm to rank the similarities between candidates and user features for different users; Step 5: Generate personalized suggestions. Based on the ranking results of the candidates, select the top-ranked candidates as personalized suggestions to display to the user, and formulate strategies based on specific learning needs. Step 6: Feedback and iterative update, continuously update user features and similarity calculation models based on user feedback information.

[0025] An English word memory training system based on artificial intelligence and speech recognition technology, such as Figure 5 As shown, it is implemented based on the following working principle: The system first establishes a database containing a large number of English words as needed. The database connects to the Internet to capture existing English dictionaries or online English resources, and extracts and classifies words from the corpus, such as common English words, CET-4 words, CET-6 words, and postgraduate entrance examination words, etc., and expands the content of related words so that the words can be combined with sentences, and at the same time establishes related synonym and antonym models. Then the user operates the content of the word memory training through the user interface, selects in the word range selection unit according to his own status, and then selects the appropriate difficulty and content in the training mode selection unit according to his own preferences. It should be noted that the user interface can be in the form of a web application, mobile application or computer software, and then provides personalized learning according to the user's learning habits and abilities. Learning settings allow users to customize their daily learning time, learning speed, etc. After the selection is completed, the system will provide corresponding training materials and test questions. During the training process, the user's voice is recognized by the voice recognition engine and converted into text for comparison and evaluation. At the same time, the accuracy of pronunciation is corrected in real time, allowing users to adjust their pronunciation in real time and improve the pronunciation of words. Then, according to the user's learning situation and performance, the system will track and use similarity calculations to make personalized learning plans and evaluations based on the calculation results. Intelligent recommendations and adjustments to the difficulty and content of the next training will help users memorize English words more effectively. After a study, the system will promptly give the user learning feedback and evaluation results, including accuracy, progress, and error analysis, etc. Users can make adjustments based on the feedback.

[0026] The above embodiments are provided for persons familiar with the art to implement or use the present invention. Personnel familiar with the art can make various modifications or changes to the above embodiments without departing from the concept of the present invention. Therefore, the protection scope of the present invention is not limited to the above embodiments, but should be the maximum scope of the innovative features mentioned in the claims.

Claims

1. An English word memory training system based on artificial intelligence and speech recognition technology, characterized in that: Included are: A word learning and memory module, which is connected to the Internet to obtain and provide a complete word library, including common English words, CET-4 words, CET-6 words, and postgraduate entrance examination words, etc. Users can learn words and perform memory training by selecting a favorite training mode; A pronunciation evaluation module, which uses speech recognition technology to monitor the user's pronunciation, and performs pronunciation accuracy evaluation processing, analyzes the user's pronunciation and provides timely feedback to help the user correct pronunciation errors in real time; The progress tracking module is configured to record the user's learning progress and performance, and provide personalized learning suggestions and recommendations based on the user's characteristics and needs, and make adaptive adjustments to the user's level and learning goals.

2. The English word memory training system based on artificial intelligence and speech recognition technology according to claim 1 is characterized in that: The word learning and memory module includes: A database, the database is connected to the Internet, and acquires and establishes a complete English word model; A user interface, wherein the user interface adopts a simple operation mode so that the user will not have difficulty in using the user interface, and the user can select the training mode of his / her preference in the user interface; A word range selection unit, wherein the word range selection unit is configured to select a word range suitable for the user's current learning range before performing system learning and memory, such as common English words, CET-4 words, CET-6 words, and postgraduate entrance examination words.

3. The English word memory training system based on artificial intelligence and speech recognition technology according to claim 2 is characterized in that: The user interface includes a training mode selection unit, and the training mode selection unit includes: A word-by-word reading mode, in which the user follows the system to read the word word by word, so that the user can grasp the pronunciation of the word more accurately; Dictation mode, which includes Chinese listening to English, English listening to Chinese, word splitting, etc., so that users can deeply memorize words during the learning and memorization process and deepen the memory effect; Fill-in-the-blank mode, which is configured to display part of the word content or Chinese translation, and the user completes the spelling of the word according to the prompt; A difficulty selection unit is configured so that the user can adjust the difficulty of the above-mentioned mode according to his / her own learning situation and acceptance ability, so that the training difficulty is more matched with his / her own ability.

4. The English word memory training system based on artificial intelligence and speech recognition technology according to claim 1 is characterized in that: The pronunciation assessment module includes: A speech recognition engine, wherein the speech recognition engine uses speech recognition technology to capture and analyze the user's pronunciation; A natural language processing module serves the speech recognition engine and is configured to enable the speech recognition engine to understand, process and generate human language.

5. The English word memory training system based on artificial intelligence and speech recognition technology according to claim 4 is characterized in that: The natural language processing module includes: A word embedding model, which is used to represent text as a low-dimensional vector to capture the semantic information of words; Sequence models, which are used for tasks such as text classification, sentiment analysis, and named entity recognition; The language model uses training and fine-tuning strategies to further optimize and improve language understanding.

6. The English word memory training system based on artificial intelligence and speech recognition technology according to claim 1 is characterized in that: The progress tracking module includes a personalized adjustment unit, which is configured to record the user's learning progress and performance and establish a corresponding data model, and then perform analysis and processing based on the data model to provide corresponding adjustment feedback for different user groups.

7. The English word memory training system based on artificial intelligence and speech recognition technology according to claim 6 is characterized in that: The personalized adjustment unit performs the personalized recommendation process in the following main steps: Step 1: Data collection and preprocessing: collect user-related data, such as user historical behavior data and personal data, and then preprocess the user-related data, including data cleaning, feature extraction and conversion; Step 2: User feature representation: construct a user feature vector based on the preprocessed user data to describe the user's feature label; Step 3: Similarity calculation, calculating the similarity between user features and candidate items, such as learning content, learning results, recommended content, etc., using the Euclidean distance algorithm or the cosine similarity algorithm; Step 4: Ranking of candidates: using collaborative filtering algorithm to rank the similarities between candidates and user features for different users; Step 5: Generate personalized suggestions. Based on the ranking results of the candidates, select the top-ranked candidates as personalized suggestions to display to the user, and formulate strategies based on specific learning needs. Step 6: Feedback and iterative update, continuously update user features and similarity calculation models based on user feedback information.

8. The English word memory training system based on artificial intelligence and speech recognition technology according to claim 1 is characterized in that: It also includes a content expansion module, which is configured to provide extended information such as sentence examples, synonyms and antonyms, context training, etc. to assist users in understanding and using words.

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